{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to get the data, run mapanalysis.ts first with ts-node mapanalysis.ts\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "resources = ['wood', 'coal', 'uranium']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_json(\"mapgendistv2.2.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "rows = []\n",
    "for k in df.keys():\n",
    "    for r in df[k]['resources']:\n",
    "        rows.append((r['wood'], r['coal'], r['uranium'], r['woodTiles'], r['coalTiles'], r['uraniumTiles'], k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(np.array(rows), columns=\n",
    "                    ['wood', 'coal', 'uranium', 'woodTiles', 'coalTiles', 'uraniumTiles', 'size']\n",
    "                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,10))\n",
    "ax = None\n",
    "for j, size in enumerate([12,16,24,32]):\n",
    "\n",
    "    for i, r in enumerate(resources):\n",
    "        k = (i+1 + j*len(resources))\n",
    "        ax = plt.subplot(4, 3, k, sharex=ax, sharey=ax)\n",
    "        plt.hist(data[data['size'] == size][r], bins=15)\n",
    "        plt.title(f\"Histogram of amount of {r} in a {size}x{size} map\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>size</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2555.0</td>\n",
       "      <td>27.159295</td>\n",
       "      <td>7.140665</td>\n",
       "      <td>16.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2369.0</td>\n",
       "      <td>34.889827</td>\n",
       "      <td>11.166965</td>\n",
       "      <td>18.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2580.0</td>\n",
       "      <td>60.993023</td>\n",
       "      <td>19.220924</td>\n",
       "      <td>20.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2496.0</td>\n",
       "      <td>99.000801</td>\n",
       "      <td>26.364426</td>\n",
       "      <td>34.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>248.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count       mean        std   min   25%   50%    75%    max\n",
       "size                                                              \n",
       "12    2555.0  27.159295   7.140665  16.0  22.0  26.0   30.0   78.0\n",
       "16    2369.0  34.889827  11.166965  18.0  26.0  32.0   40.0   96.0\n",
       "24    2580.0  60.993023  19.220924  20.0  48.0  58.0   72.0  164.0\n",
       "32    2496.0  99.000801  26.364426  34.0  80.0  96.0  116.0  248.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('size').describe()['woodTiles']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>size</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2555.0</td>\n",
       "      <td>7884.279452</td>\n",
       "      <td>1139.685293</td>\n",
       "      <td>3558.0</td>\n",
       "      <td>7141.0</td>\n",
       "      <td>7640.0</td>\n",
       "      <td>8409.0</td>\n",
       "      <td>14552.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2369.0</td>\n",
       "      <td>9076.752216</td>\n",
       "      <td>1696.483093</td>\n",
       "      <td>3830.0</td>\n",
       "      <td>7818.0</td>\n",
       "      <td>8786.0</td>\n",
       "      <td>9988.0</td>\n",
       "      <td>17662.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2580.0</td>\n",
       "      <td>12992.510078</td>\n",
       "      <td>2883.605504</td>\n",
       "      <td>5790.0</td>\n",
       "      <td>10958.0</td>\n",
       "      <td>12652.0</td>\n",
       "      <td>14662.5</td>\n",
       "      <td>28770.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2496.0</td>\n",
       "      <td>18686.880609</td>\n",
       "      <td>3964.116790</td>\n",
       "      <td>8712.0</td>\n",
       "      <td>15810.0</td>\n",
       "      <td>18302.0</td>\n",
       "      <td>21202.5</td>\n",
       "      <td>41972.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count          mean          std     min      25%      50%      75%  \\\n",
       "size                                                                         \n",
       "12    2555.0   7884.279452  1139.685293  3558.0   7141.0   7640.0   8409.0   \n",
       "16    2369.0   9076.752216  1696.483093  3830.0   7818.0   8786.0   9988.0   \n",
       "24    2580.0  12992.510078  2883.605504  5790.0  10958.0  12652.0  14662.5   \n",
       "32    2496.0  18686.880609  3964.116790  8712.0  15810.0  18302.0  21202.5   \n",
       "\n",
       "          max  \n",
       "size           \n",
       "12    14552.0  \n",
       "16    17662.0  \n",
       "24    28770.0  \n",
       "32    41972.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('size').describe()['wood']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
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       "    <tr>\n",
       "      <th>size</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>12</th>\n",
       "      <td>12775.0</td>\n",
       "      <td>26412.704501</td>\n",
       "      <td>10099.253475</td>\n",
       "      <td>15130.0</td>\n",
       "      <td>17720.0</td>\n",
       "      <td>23340.0</td>\n",
       "      <td>30050.0</td>\n",
       "      <td>81290.0</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11845.0</td>\n",
       "      <td>33214.850148</td>\n",
       "      <td>14109.619523</td>\n",
       "      <td>15240.0</td>\n",
       "      <td>22790.0</td>\n",
       "      <td>29630.0</td>\n",
       "      <td>40370.0</td>\n",
       "      <td>111770.0</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>12900.0</td>\n",
       "      <td>52524.779070</td>\n",
       "      <td>22788.373824</td>\n",
       "      <td>15190.0</td>\n",
       "      <td>35267.5</td>\n",
       "      <td>49850.0</td>\n",
       "      <td>64705.0</td>\n",
       "      <td>175350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>12480.0</td>\n",
       "      <td>77280.645032</td>\n",
       "      <td>29742.338222</td>\n",
       "      <td>16030.0</td>\n",
       "      <td>56610.0</td>\n",
       "      <td>73770.0</td>\n",
       "      <td>94647.5</td>\n",
       "      <td>231690.0</td>\n",
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       "        count          mean           std      min      25%      50%      75%  \\\n",
       "size                                                                            \n",
       "12    12775.0  26412.704501  10099.253475  15130.0  17720.0  23340.0  30050.0   \n",
       "16    11845.0  33214.850148  14109.619523  15240.0  22790.0  29630.0  40370.0   \n",
       "24    12900.0  52524.779070  22788.373824  15190.0  35267.5  49850.0  64705.0   \n",
       "32    12480.0  77280.645032  29742.338222  16030.0  56610.0  73770.0  94647.5   \n",
       "\n",
       "           max  \n",
       "size            \n",
       "12     81290.0  \n",
       "16    111770.0  \n",
       "24    175350.0  \n",
       "32    231690.0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data.groupby('size').describe()['coal'] * 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>12</th>\n",
       "      <td>102200.0</td>\n",
       "      <td>115307.303327</td>\n",
       "      <td>70032.486269</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>46960.0</td>\n",
       "      <td>90640.0</td>\n",
       "      <td>140000.0</td>\n",
       "      <td>491360.0</td>\n",
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       "      <th>16</th>\n",
       "      <td>94760.0</td>\n",
       "      <td>170803.815956</td>\n",
       "      <td>104096.708357</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>88160.0</td>\n",
       "      <td>138480.0</td>\n",
       "      <td>223360.0</td>\n",
       "      <td>701680.0</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>103200.0</td>\n",
       "      <td>317253.798450</td>\n",
       "      <td>180523.521590</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>176880.0</td>\n",
       "      <td>301200.0</td>\n",
       "      <td>422840.0</td>\n",
       "      <td>1017120.0</td>\n",
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       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>99840.0</td>\n",
       "      <td>523178.782051</td>\n",
       "      <td>267406.733405</td>\n",
       "      <td>40080.0</td>\n",
       "      <td>311620.0</td>\n",
       "      <td>483680.0</td>\n",
       "      <td>699120.0</td>\n",
       "      <td>1636480.0</td>\n",
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       "         count           mean            std      min       25%       50%  \\\n",
       "size                                                                        \n",
       "12    102200.0  115307.303327   70032.486269  40000.0   46960.0   90640.0   \n",
       "16     94760.0  170803.815956  104096.708357  40000.0   88160.0  138480.0   \n",
       "24    103200.0  317253.798450  180523.521590  40000.0  176880.0  301200.0   \n",
       "32     99840.0  523178.782051  267406.733405  40080.0  311620.0  483680.0   \n",
       "\n",
       "           75%        max  \n",
       "size                       \n",
       "12    140000.0   491360.0  \n",
       "16    223360.0   701680.0  \n",
       "24    422840.0  1017120.0  \n",
       "32    699120.0  1636480.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data.groupby('size').describe()['uranium'] * 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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       "      <td>3.177598</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>4.727941</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>32.0</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2580.0</td>\n",
       "      <td>14.434109</td>\n",
       "      <td>8.208834</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>46.0</td>\n",
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       "    <tr>\n",
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       "      <td>2496.0</td>\n",
       "      <td>23.814103</td>\n",
       "      <td>12.156919</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>74.0</td>\n",
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       "       count       mean        std  min   25%   50%   75%   max\n",
       "size                                                           \n",
       "12    2555.0   5.243053   3.177598  2.0   2.0   4.0   6.0  22.0\n",
       "16    2369.0   7.772056   4.727941  2.0   4.0   6.0  10.0  32.0\n",
       "24    2580.0  14.434109   8.208834  2.0   8.0  14.0  20.0  46.0\n",
       "32    2496.0  23.814103  12.156919  2.0  14.0  22.0  32.0  74.0"
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     "execution_count": 29,
     "metadata": {},
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   ],
   "source": [
    "data.groupby('size').describe()['uraniumTiles']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
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       "      <td>6.0</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>28.0</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>38.0</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2580.0</td>\n",
       "      <td>18.284496</td>\n",
       "      <td>7.930367</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>20.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>34.0</td>\n",
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      "text/plain": [
       "       count       mean        std  min   25%   50%   75%   max\n",
       "size                                                           \n",
       "12    2555.0   9.192955   3.490892  6.0   6.0   8.0  10.0  28.0\n",
       "16    2369.0  11.555931   4.898498  6.0   8.0  10.0  14.0  38.0\n",
       "24    2580.0  18.284496   7.930367  6.0  12.0  18.0  22.0  62.0\n",
       "32    2496.0  26.891026  10.342789  6.0  20.0  26.0  34.0  80.0"
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     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data.groupby('size').describe()['coalTiles']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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